Brief Bio

I received my Bachelor's degree (2011) in Automation from Xi'an Jiaotong University, and my Master's degree (2014) in Electrical Engineering and Information Technology from the Technical University of Munich.
From 2014 to 2016 I worked as a computer vision algorithm developer for advanced driver assistance systems (ADAS) at Continental. Since March 2016 I am a PhD student in the Computer Vision Group at the Technical University of Munich, headed by Professor Daniel Cremers. My research interests include visual SLAM and visual 3D reconstruction, as well as their combinations with semantic information.

Research Interests

Semantic VO / SLAM

Direct Shape This video shows the basic idea and some results of our paper "DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation". In this work, we estimate the 3D poses and shapes of cars based on a single stereo image pair. Note that the point clouds in the video are only for visualization purpose, they are not used in our method. For more details please refer to the paper arXiv and the project page

Classic VO / SLAM

SLAM extension to Stereo DSO After the ICCV 2017 deadline, we extended our method to a SLAM system with additional components for map maintenance, loop detection and loop closure. Our performance on KITTI is further boosted a little, as shown by the plots in the video. (Project Page)

LDSO: Direct Sparse Odometry with Loop Closure In this project we integrate feature points into DSO to improve the repeatability of the sampled points, enabling loop closure candidate detection using BoW and loop closure based on pose graph optimization. For more details and access to the code, please visit the Project Page.

Deep Learning Boosted VO / SLAM

Deep Virtual Stereo Odometry (DVSO) In this project we design a novel deep network and train it in a semi-supervised way to predict depth map from single image, and integrate the depth map into DSO as virtual stereo measurement. Being a monocular VO approach, DVSO achieves comparable performance to the state-of-the-art stereo methods. (Project Page)

Camera Calibration

Online Photometric Calibration We've conducted a project to achieve online photometric calibration, where the exposure times of consecutive frames, the camera response function, and the camera vignetting factors can be recovered in real-time. Experiments show that our estimations converge to the ground truth after only a few seconds. Our approach can be used either offline for calibrating existing datasets, or online in combination with state-of-the-art direct visual odometry or SLAM pipelines. For more details please check our paper "Online Photometric Calibration of Auto Exposure Video for Realtime Visual Odometry and SLAM". (Project Page)